Introduction

What if you could make your incident response process more scientific using nothing more than paper, a pen, and a clipboard?

In an age of dashboards, SIEMs, and automation, that sounds backwards. Yet an analog “incident railway waiting list”—a literal paper queue of pending work—can be an ideal laboratory for tiny reliability experiments. Instead of pushing changes straight into your tools and workflows, you can try them first on a simple, controllable, observable paper process.

This post explores how to turn that analog queue into a scientific playground for improving intrusion analysis and incident response. We’ll look at:

• What it means for incident response to be truly scientific
• How classical philosophy (yes, including Aristotle) helps define “science” in this context
• How to design lean, hypothesis-driven experiments with a paper waiting list
• How to connect those micro-experiments to system-level reliability models like fault trees and reliability block diagrams

From “Art” to “Science” in Incident Response

Incident response and intrusion analysis are often described as crafts. Skilled responders "just know" what to look for, which logs to pull, which alerts to ignore. That intuition is valuable—but it’s also fragile and hard to scale.

To move from art to science, we need to:

1. Formalize methods – Make steps explicit: what’s done, in what order, with what information.
2. State assumptions clearly – For example: “We assume that X log source is complete for authentication failures.”
3. Define validation criteria – What counts as “better”? Faster containment? Fewer repeat incidents? Higher availability?

Without these, it’s impossible to know whether a new detection rule, triage practice, or handoff policy is genuinely better—or just feels better.

An analog incident waiting list is a surprisingly good place to start formalizing: every incident you write down must have columns, categories, and rules. That forces you to define what you’re doing and how you’re measuring it.

What Does “Scientific” Actually Mean Here?

It’s easy to say “be more scientific.” It’s harder to pin down what that means for incident response and reliability. Classical philosophy of science, going back to Aristotle, gives us useful anchors:

• Knowledge vs. opinion: For Aristotle, science (epistēmē) is justified, structured knowledge—understanding why something happens, not just that it happens.
• Causes and explanations: Science is about causes. For us: “Why did this incident recur?”, “Why does our MTTR vary by 5x between teams?”
• General laws or regularities: Science seeks patterns that hold reliably, not one-off anecdotes.

From this, we can derive practical scientific standards for incident and reliability work:

1. Repeatability – If another team applies the same process to similar incidents, they should get similar outcomes.
2. Falsifiability – You write hypotheses that can be wrong, e.g., “If we introduce step X, mean time to triage (MTTT) will decrease by at least 15%.”
3. Well-defined concepts – Everyone agrees what “triaged,” “contained,” or “resolved” actually mean.

Your analog waiting list can force these definitions. If you have a column named “Triaged At,” you must decide what event counts as triage. That decision turns a vague craft step into something measurable and, therefore, testable.

The Analog Incident Railway Waiting List

Imagine your incident workflow as a railway, and your incidents as train cars waiting on a siding track before being processed. The analog waiting list is:

• A physical sheet (or notebook) where every incident is logged as it arrives.
• Each incident occupies one row, like a train car in line.
• Columns capture the minimal, structured data you care about.

A simple first version might include:

• Incident ID / short description
• Time detected
• Time first touched (triage start)
• Time contained
• Time resolved
• Category (e.g., phishing, credential abuse, endpoint malware)
• Source (alerting system, user report, external notification)

You place the sheet in a central, visible location. Each time work moves, someone updates the row by hand. It’s deliberately inconvenient enough to keep the data compact and deliberate, and simple enough that changing the process is low-risk and reversible.

Designing Tiny Lean Reliability Experiments

With the analog queue in place, you can begin running lean experiments on your process. A lean experiment is:

1. Hypothesis-driven – You explicitly state what you expect and why.
2. Small and controlled – Try it for a short period or subset of incidents.
3. Measurable – You know in advance what metrics will move, and by how much.

Step 1: Choose a Clear Hypothesis

Examples:

• If we add a 3-minute “context-gathering” checklist at triage, then average containment time for phishing incidents will decrease by 20% over two weeks.
• If we route all credential-abuse alerts through a specialized on-call responder, then variance in response time will drop by half.

The analog list helps because every step (triage, containment, resolution) is an explicit timestamp.

Step 2: Define Your Measurement Window

You don’t need months of data to learn something. Pick a period like 2–4 weeks or the next 30 incidents of a certain type. Write the experiment window directly at the top of the paper:

"Experiment #3: Context checklist for phishing. Duration: 2026-02-01 to 2026-02-15. Target: -20% mean containment time."

Step 3: Implement the Change on Paper First

Resist the urge to reconfigure your ticketing system or automation platform. Instead:

• Add a new column or mark on the waiting list (e.g., “Checklist completed? Y/N”).
• Add a small paper checklist attached to the board.

If the experiment fails, you erase a column and discard a checklist, not roll back a production change.

Step 4: Analyze and Decide

At the end of the window, compute simple statistics from the sheet:

• Average and median time from detected → triaged
• Average and median time from triaged → contained
• Incident counts per category

Then ask:

• Did the metric move as expected?
• Is the change repeatable, or did one odd incident skew results?
• Did we trade off something important (e.g., faster triage but more false positives)?

Only when an analog experiment shows clear, repeatable improvement should you codify it in your digital tools.

Connecting Micro-Experiments to System Reliability

Incidents and response processes don’t live in isolation. They influence system-level availability and reliability, and vice versa. To reason about that, reliability engineers use:

• Fault Tree Analysis (FTA) – A top-down method where you start from an undesired event (e.g., "Customer login unavailable") and decompose it into combinations of lower-level failures.
• Reliability Block Diagrams (RBDs) – Models that represent your system as blocks in series/parallel, each with probabilities of failure, to compute overall availability.

Your analog queue experiments generate input data for these models:

• Mean Time to Detect (MTTD) and Mean Time to Repair/Recover (MTTR) from timestamps
• Frequency and distribution of specific incident types
• Empirical rates of human error, rework, or mis-triage

Alongside other data sources—tests, field data, logging, and engineering handbooks—this gives a grounded basis for modeling.

Example: How an Analog Experiment Feeds a Fault Tree

Suppose your fault tree for “Customer login failure” includes a branch:

• Slow response to credential stuffing attack → prolonged outage

Your analog experiment might be:

Add a pre-authorized response playbook for credential attacks, expected to reduce MTTR by 30%.

By capturing before-and-after MTTR on the paper queue, you estimate the new response time distribution. That, in turn, adjusts the probability that such an incident causes an extended outage in your fault tree. Your tiny analog tweak now has a quantified effect on system-level risk.

Grounding Models in Real-World Evidence

Scientific reliability work lives or dies on the quality of its input data. Paper experiments don’t stand alone; they complement other sources:

• Tests and drills – Chaos experiments, failover tests, red-team exercises.
• Prior operations data – Historical logs from ticketing systems, monitoring, SIEM.
• Field data – Vendor incident reports, community data on attack patterns.
• Engineering data handbooks – Reference failure rates for hardware, typical MTBF/MTTR benchmarks.

The analog waiting list helps you:

• Fill gaps where digital systems don’t yet capture the nuance (e.g., “triage actually started here, not when the ticket was created”).
• Experiment with new metrics before adding them to your tools.
• Cross-check automated timestamps against human reality.

In other words, it becomes a ground-truth surface where your theoretical models and your day-to-day operations meet.

Conclusion: Why Bother with Paper in a Digital World?

The analog incident railway waiting list is not nostalgia. It’s a deliberately low-tech instrument for:

• Forcing clarity in definitions and assumptions
• Running small, falsifiable, repeatable experiments on process and reliability
• Generating clean, interpretable data to feed fault trees and reliability block diagrams
• Bridging the gap between philosophical ideas of science and the gritty work of intrusion analysis

By first experimenting on paper, you:

• Reduce risk—bad ideas die on the whiteboard instead of in production.
• Reduce waste—you only automate what you know actually helps.
• Increase understanding—your team can see, literally, how work flows and where it stalls.

Turning incident response into a scientific discipline doesn’t require a new platform or AI. It starts with being explicit about what you’re doing, why you think it works, and how you’ll know if you’re wrong.

Sometimes, the most powerful laboratory for that transformation is a simple sheet of paper tracking trains of incidents as they wait on the reliability railway—an analog queue for deeply modern experiments.